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Updated: Apr 28, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Integration Analysis of Bayesian and Machine Learning for Heterogeneity, Biomarkers, and Optimal Combination Regimens
Yingge He1, Changqing Gao2, Shiyan Zhang2
1Department of Plastic and Cosmetic Surgery, Xinqiao Hospital, Army Medical University, Chongqing, China.
Abstract:
The efficacy of PD-1 inhibitor pucotenlimab (HX008) in solid tumors exhibits heterogeneity. This study integrated data from 6 clinical trials (covering gastric/gastroesophageal junction cancer, triple-negative breast cancer, melanoma, and dMMR/MSI-H solid tumors) using Bayesian meta-analysis, machine learning (optimal XGBoost AUC = 0.86), and network meta-analysis to construct an integrated "efficacy-prediction-safety" framework. Bayesian analysis showed pucotenlimab significantly improved outcomes versus control (ORR OR = 4.82, 95% CrI: 3.65-6.38; PFS HR = 0.41, 0.32-0.52; OS HR = 0.37, 0.26-0.51). Subgroups revealed TNBC patients with gemcitabine/cisplatin achieved highest ORR (80.6%, 62.5%-92.6%), while mucosal melanoma showed lowest response (8.7%, 1.1%-28.0%). Combination therapy demonstrated superior efficacy to monotherapy (ORR OR: 5.91 vs. 2.35). Machine learning identified 4 efficacy biomarkers (KMT2D mutation, post-treatment NLR decrease, PD-L1 CPS ≥ 1, high eotaxin) and 3 irAE risk factors (baseline NLR ≥ 4, irinotecan combination, high VEGF). Network analysis recommended regimens: gemcitabine/cisplatin for TNBC (SUCRA = 95.7%), oxaliplatin/capecitabine for G/GEJ cancer (ORR = 60.0% vs. irinotecan 27.6%, HR = 0.45). The integrated model classified high-benefit (≥ 3 points; ORR 78.2%) and low-benefit (≤ 0 points; ORR 28.3%) groups, plus high-risk (≤ -2 points; grade ≥ 3 irAEs 41.2%) and low-risk (≥ 1 point; irAEs 3.5%) groups, validated by decision curve analysis. This defines precise application scenarios and provides an extensible analytical paradigm.
